Methods and systems for assessing healing of tissue

ABSTRACT

Methods and systems for assessing tissue of a subject include receiving a time series of signal intensity data capturing the transit of an imaging agent through tissue over a period of time, wherein the tissue comprises a plurality of calculation regions and wherein signal intensity in each calculation region over the period of time may be approximated by a time-intensity curve corresponding to the calculation region; determining, for each calculation region, a coefficient value that is related to at least a portion of the time-intensity curve corresponding to the calculation region; and converting the coefficient values across the plurality of calculation regions into a coefficient-derived image map.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 62/222,630 filed on Sep. 23, 2015, entitled “METHODS AND SYSTEMS FORASSESSING TISSUE TO ESTABLISH A PROGNOSIS FOR TISSUE HEALING,” which ishereby incorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

Poor tissue perfusion has an adverse effect on the healing process oftissue. To increase the chances of determining whether successfulhealing of, for example, acute and chronic wounds will occur, cliniciansmust correctly assess blood flow and tissue perfusion in and around thewound site. Furthermore, the ability to predict the potential forhealing and the timeline of healing is also important. Usually, visualassessment of the wound, measurement of a reduction in wound area,and/or the percentage of wounds healed within a defined period is usedas a scoring system for establishing a wound treatment protocol.

Certain advanced practices have begun to use imaging technologies suchas fluorescence imaging technologies for assessing blood flow and/ortissue perfusion and establishing a prognosis for wound healing.Fluorescence imaging technologies may, for example, employ theadministration of a bolus of an imaging agent (such as, for example,indocyanine green which binds with blood proteins in a subject) thatsubsequently circulates throughout the subject's vasculature and emits afluorescence signal when illuminated with the appropriate excitationlight. Fluorescence imaging systems acquire images of the emittedimaging agent fluorescence as the imaging agent bolus traverses thesubject's tissue in the field of view. The images are typically acquiredas the bolus enters the tissue through arterial vessels, travels throughthe tissue's microvasculature, and exits the tissue through the venousvessels. When the images are displayed as video on a monitor, cliniciansmay observe this imaging agent transit in the vasculature represented asvariations in fluorescence intensity with time. Based on their visualperception of the fluorescence intensity, clinicians may make arelative, qualitative determination regarding the blood flow and/orperfusion status of the tissue and its subsequent healing potential.However, a qualitative visual evaluation of such images is not alwayssufficient for a number of reasons, particularly in instances where thevisual information is ambiguous. For instance, such visual evaluation islimited since many parameters, such as image brightness, image contrastand image noise, can be affected by factors other than the blood flowand/or perfusion properties of the tissue. Moreover, mere visualevaluation is subjective (e.g., visual evaluation may vary fromclinician to clinician, one clinician's visual evaluation protocol mayvary somewhat from patient to patient and/or from imaging session toimaging session) and does not support a standardized protocol forassessing blood flow and/or tissue perfusion, and/or for assessinghealing of tissue (e.g., progress of healing, efficacy of clinicalintervention, etc.). Finally, due to a clinician's lack of memory orinaccurate recollection of previous visual assessments, it can bechallenging to reliably and consistently compare and track blood flow,perfusion, and/or healing status of a patient over time across multipleimaging sessions.

The assessment of perfusion dynamics and a prognosis of tissue healingis also important in other clinical applications aside from wound care,such as, for example, pre-surgical evaluation of patients undergoingplastic or reconstructive procedures (e.g., skin flap transfers). Forinstance, it is desirable for fluorescence imaging systems to possessthe data processing capabilities which consider parameters that reflectrelevant perfusion dynamics and facilitate providing a prognosis fortissue healing. Furthermore, it is desirable for fluorescence imagingsystems to present image data to the clinician in a manner that providessuch information in a convenient and easily understood fashion.

It is therefore desirable to provide a tool that can aid the clinicianin providing an accurate and reliable prognosis of healing potential ofa tissue, chronicity or both. This will assist, for example, in ensuringthat a correct diagnosis of the tissue is given, and that appropriatecare is provided in a timely manner, therefore improving healing timeand patient quality of life, and alleviating economic burden onhealthcare systems.

BRIEF SUMMARY OF THE INVENTION

Described herein are variations of systems and methods for assessinghealing of tissue of a subject. More in general are described hereinvariations of systems and methods for use in medical imaging, such asfor assessing healing of tissue of a subject. It will be appreciatedthat these variations also relate to systems and methods for providingand/or presenting data usable as an aid in assessing healing of tissueof a subject. Generally, in one variation, a system for assessinghealing of tissue of a subject includes one or more processors andmemory having instructions stored thereon. The instructions, whenexecuted by the one or more processors, cause the system to receive atime series of signal intensity data capturing the transit of an imagingagent through tissue over a period of time, wherein the time series ofsignal intensity data define a plurality of calculation regions. Signalintensity in each calculation region over the period of time may beapproximated by a time-intensity curve corresponding to that calculationregion. The at least one calculation region may, for instance, bedefined by one pixel or voxel. The instructions further cause the systemto determine, for each calculation region, a coefficient value that isrelated to at least a portion of the time-intensity curve correspondingto the calculation region. The coefficient values across the pluralityof calculation regions can be converted into a coefficient-derived imagemap. The system may include a light source that provides an excitationlight to induce fluorescence emission from a fluorescence imaging agentin the tissue, and/or an image acquisition assembly that generates thetime series of signal intensity data based on the fluorescence emissionsuch as, for example, a time series of fluorescence angiography imagesbased on the fluorescence emission. Furthermore, the system may includea display for displaying the coefficient-derived image map and/or ananatomical image of the tissue. In other aspects, the system may beconfigured to perform at least a portion of the methods described hereinfor assessing healing of tissue of a subject.

In some variations, the coefficient value may characterize a shape ofthe time-intensity curve, or a portion thereof, such as a region ofincreasing slope of the time-intensity curve (e.g., an arterial phase ofthe time-intensity curve), a region of decreasing slope of thetime-intensity curve (e.g., a venous phase of the time-intensity curve),or a combination thereof. The coefficient values for the calculationregions may be correlated into a coefficient-derived image map based on,for example, a conversion of each of the coefficient values into arespective pixel intensity. The resulting coefficient-derived image mapmay, in some variations, be indicative of an actual or suspected woundand allow for predictive assessment of healing of tissue of the subject.

Generally, one variation, a method for assessing healing of tissue of asubject includes receiving a time series of signal intensity datacapturing the transit of an imaging agent through tissue over a periodof time, wherein the tissue comprises a plurality of calculation regionsand wherein signal intensity in each calculation region over the periodof time may be approximated by a time-intensity curve corresponding tothe calculation region, determining for each calculation region acoefficient value that is related to at least a portion of thetime-intensity curve corresponding to the calculation, and convertingthe coefficient values across the plurality of calculation regions intoa coefficient-derived image map. The at least one calculation regionmay, for instance, be defined by one pixel or voxel. The method may beperformed at a computer system including one or more processors andmemory.

As in the system briefly described above, the coefficient value maycharacterize a shape of the time-intensity curve, or a portion thereof,such as a region of increasing slope of the time-intensity curve (e.g.,an arterial phase of the time-intensity curve), a region of decreasingslope of the time-intensity curve (e.g., a venous phase of thetime-intensity curve), or a combination thereof. Converting thecoefficient values into a coefficient-derived image map may comprisecorrelating each coefficient value with an intensity value. Thecoefficient-derived image map, and/or other images and info such as ananatomical image of the tissue, may be displayed and/or superimposed onone another on a display.

The method may further comprise assessing tissue of the subject based atleast in part on the coefficient-derived image map. The assessed tissuemay include, for example, a wound and/or peri-wound in the tissue.Assessing the tissue may comprise generating a quantitative predictor ofthe progress of healing of tissue, efficacy of clinical intervention, ora combination thereof based on at least a portion of thecoefficient-derived image. The quantitative predictor may be based on asingle coefficient-derived image, though a plurality ofcoefficient-derived images may be obtained and compared over time (e.g.,based on a plurality of time series of signal intensity data capturedover time) in order to generated other assessments.

In some variations, the method may further comprise determining, foreach calculation region, a second coefficient value that is related toat least a second portion of the time-intensity curve corresponding tothe calculation region, and converting the second coefficient valuesacross the plurality of calculation regions into a secondcoefficient-derived image map. For instance, the firstcoefficient-derived image map may be an arterial coefficient-derivedimage map and the second coefficient-derived image map may be a venouscoefficient-derived image map. In these variations, assessing tissue ofthe subject may comprise generating a quantitative predictor of theprogress of healing of tissue, efficacy of clinical intervention, or acombination thereof, based on the first and second coefficient-derivedimage maps. For example, generating the quantitative predictor maycomprise comparing the area of a first selected region in the firstcoefficient-derived image map to the area of a second selected region inthe second coefficient-derived image map, where the selected regionsrepresent an actual or suspected wound (or other abnormal or suspectedabnormal arterial or venous activity). Accordingly, the quantitativepredictor may, for example, include a ratio of the areas of the firstand second selected regions.

Additionally, the method may further include generating the time seriesof signal intensity data using a fluorescence imaging system thatcaptures transit of the imaging agent through tissue over a period oftime. For example, the imaging agent may include indocyanine green,fluorescein isothiocyanate, rhodamine, phycoerythrin, phycocyanin,allophycocyanin, ophthaldehyde, fluorescamine, rose Bengal, trypan blue,fluoro-gold, green fluorescence protein, a flavin, methylene blue,porphysomes, cyanine dye, IRDDye800CW, CLR 1502 combined with atargeting ligand, OTL38 combined with a targeting ligand, or acombination thereof.

It will be appreciated that any of the variations, aspects, features andoptions described in view of the systems apply equally to the methodsand vice versa. It will also be clear that any one or more of the abovevariations, aspects, features and options can be combined.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 is an illustrative block diagram of an exemplary method forassessing healing of tissue of a subject.

FIG. 2A is an illustrative depiction of a time series of images. FIG. 2Bis an illustrative depiction of a time-intensity curve generated for acalculation region in the time series of images.

FIG. 3 is an exemplary time-intensity curve with a plurality ofexemplary parameters that approximate or otherwise characterize thetime-intensity curve.

FIG. 4 is an illustrative block diagram of another exemplary method forassessing healing of tissue of a subject.

FIGS. 5A-5D illustrate a schematic representation of the wound healingprocess as depicted in coefficient-derived image maps.

FIG. 6 is an illustrative depiction of an exemplary fluorescence imagingsystem configured to assess healing of tissue of a subject.

FIG. 7 is an illustrative depiction of an exemplary illumination moduleof a fluorescence imaging system configured to assess healing of tissueof a subject.

FIG. 8 is an exemplary camera module of a fluorescence imaging systemconfigured to assess healing of tissue of a subject.

FIGS. 9-10 depict images of a control rat that were generated accordingto an exemplary embodiment. FIGS. 9A-9D depict results for the controlrat 24 hours after removal of pressure magnets. FIGS. 10A-10D depictresults for the control rat 48 hours after removal of pressure magnets.

FIGS. 11-13 depict images of a rat with minor wounds induced by pressuremagnets, where results were generated according to an exemplaryembodiment. FIGS. 11A-11D depict results for the rat 3 hours afterremoval of pressure magnets. FIGS. 12A-12D depict results for the rat 24hours after removal of pressure magnets. FIGS. 13A-13D depict resultsfor the rat 48 hours after removal of pressure magnets.

FIGS. 14-19 depict images of a rat with severe wounds induced bypressure magnets, where results were generated according to an exemplaryembodiment. FIGS. 14A-14D depict results for the rat immediately afterremoval of pressure magnets. FIGS. 15A-15D depict results for the rat 2hours after removal of pressure magnets. FIGS. 16A-16D depict resultsfor the rat 24 hours after removal of pressure magnets. FIGS. 17A-17Ddepict results for the rat 48 hours after removal of pressure magnets.FIGS. 18A-18D depict results for the rat 72 hours after removal ofpressure magnets. FIGS. 19A-19D depict results for the rat 8 days afterremoval of pressure magnets.

FIGS. 20A-20C depict a color image, an arterial coefficient-derivedimage, and a venous coefficient-derived image, respectively, for asevere shin ulcer wound, where the images are generated according to anexemplary embodiment relating to an application of the methods andsystems to assess healing of tissue.

FIGS. 21A-21C depict a color image, an arterial coefficient-derivedimage, and a venous coefficient-derived image, respectively, for atraumatic fracture wound, where the images are generated according to anexemplary embodiment relating to an application of the methods andsystems to assess healing of tissue.

FIGS. 22A-22C depict a color image, an arterial coefficient-derivedimage, and a venous coefficient-derived image, respectively, for anischemic wound, where the images are generated according to an exemplaryembodiment relating to an application of the methods and systems toassess healing of tissue.

FIGS. 23A-23C depict a color image, an arterial coefficient-derivedimage, and a venous coefficient-derived image, respectively, for anischemic wound, where the images are generated according to an exemplaryembodiment relating to an application of the methods and systems toassess healing of tissue.

FIGS. 24A-24C depict a maximum perfusion image, an arterialcoefficient-derived image, and a venous coefficient-derived image ofbreast tissue obtained pre-surgery, where the images are generatedaccording to an exemplary embodiment relating to an application of themethods and systems to plastic and reconstructive surgery. FIG. 24Ddepicts a color image of the breast tissue post-surgery.

FIG. 25 illustrates a venous coefficient-derived image generatedaccording to an exemplary embodiment relating to an application of themethods and systems to identify a vessel or network of vessels in theskin.

FIGS. 26A and 26B illustrate a maximum perfusion image and a venouscoefficient-derived image generated according to an exemplary embodimentrelating to an application of the methods and systems to identify avessel network and discriminate between different kinds of vessels inthe network.

DETAILED DESCRIPTION OF THE INVENTION

Reference will now be made in detail to implementations and embodimentsof various aspects and variations of the invention, examples of whichare illustrated in the accompanying drawings. Various fluorescenceimaging and/or processing systems and methods are described herein.Although at least two variations of imaging and/or processing systemsand methods are described, other variations of fluorescence imagingand/or processing systems and methods may include aspects of the systemsand methods described herein combined in any suitable manner havingcombinations of all or some of the aspects described.

One challenge in wound management, (e.g., chronic wound management) isthat the medical condition or nature of a wound can be vieweddifferently among clinicians depending, for example, on the skill andexperience of the clinician. Current techniques may provide informationabout the wound's pathological history, but fail to provide reliableindicators of viability and/or restorative potential, e.g., whetherwound and/or peri-wound (i.e., tissue surrounding the wound or adjacentthe wound) is likely to develop complications, is capable of healing,how healing progresses, and whether the treatment applied is effectiveand when it can be discontinued. Furthermore, wounds exist where nopathology is demonstrable by conventional diagnostic techniques. Variousembodiments of the methods and systems of the present inventionfacilitate producing a consistent representation (not subjective tobiases of perception) of the state of a particular target tissue (e.g.wound, peri-wound), and thus facilitate a more accurate, consistentassessment and formulation of care strategies (e.g., recommendation andassessment of efficacy of care such as, for example, topical treatments,hyperbaric oxygen therapy, assessment of the tissue pre- andpost-surgery, formulation of surgical strategy).

The methods and systems described herein may, for example, be used inwound management, plastic surgery, and/or reconstructive surgery.Examples of uses include assessment of the wound and peri-woundenvironments in the tissue, discrimination between healing andnon-healing wounds, assessment of a state of the wound, a property ofthe wound, a condition of the wound, and/or a healing status of thewound. The wound may be, for example, a surgical wound, a chronic wound,and/or an acute wound. Examples of such wounds include incisions,pressure ulcers, venous ulcers, arterial ulcers, diabetic lowerextremity ulcers, lacerations, abrasions, punctures, contusions,avulsions, cavities, burns, other injury, or any combination thereof.

Methods for Assessing Tissue

As shown in FIG. 1, an example of a method 100 for assessing tissue(e.g., assessing healing of tissue) may include: receiving a time seriesof signal intensity data 112 capturing the transit of an imaging agentthrough tissue over a period of time, wherein the tissue comprises aplurality of calculation regions and wherein signal intensity in eachcalculation region over the period of time may be approximated by atime-intensity curve corresponding to the calculation region;determining, for each calculation region, a coefficient value 114 thatis related to at least a portion of the time-intensity curvecorresponding to the calculation region; and converting the coefficientvalues across the plurality of calculation regions into acoefficient-derived image map 116. The method 100 may further includedisplaying the coefficient-derived image map on a display 118 and/orassessing tissue of the subject based at least in part on thecoefficient-derived image map 120.

At least a portion of the method may be performed by a computer systemlocated separate from a medical imaging system. For instance, some orall of the steps of receiving a time series of signal intensity data112, determining for each calculation region a coefficient value 114,converting the coefficient values across the plurality of calculationregions into a coefficient-derived image map 116, and/or assessingtissue of the subject based at least in part on the coefficient-derivedimage map 120 may be performed by a computer system at an off-sitelocation that is remote from a clinical site (e.g., where a fluorescenceimaging system is situated) or by a computer system that is located at aclinical setting but not embodied in an imaging system. In thesevariations, the time series of signal intensity data may be received asa result of a transfer of signal data from a data storage medium (e.g.,hard drive, cloud storage, etc.) or through a network communication(e.g., wired connection, Internet, wireless network based on a suitablewireless technology standard, etc.). For instance, the method mayinvolve a client-server architecture, such that an imaging system mayinclude client hardware that sends signal data to a computing server andloads processed data (e.g., coefficient-derived image map or interimoutputs of various steps of the methods described herein) back onto theimaging system. After the client hardware in the imaging system loadsthe processed data, the imaging system may further process the dataand/or display the processed data in accordance with the methodsdescribed herein.

In some variations, at least a portion of the method is performed by oneor more processors at a computer system incorporated into a medicalimaging system, such as at a clinical site. For example, some or all ofthe steps of receiving a time series of signal intensity data 112,determining for each calculation region a coefficient value 114,converting the coefficient values across the plurality of calculationregions into a coefficient-derived image map 116, and/or assessingtissue of the subject based at least in part on the coefficient-derivedimage map 120 may be performed by a computer system in a medical imagingsystem. In some of these variations, the method may further includegenerating the time series of signal intensity data 110 prior toreceiving the time series of signal intensity data.

As described above, current medical imaging technologies such asfluorescence imaging systems provide limited opportunity for cliniciansto accurately assess blood flow and/or tissue perfusion in tissue of asubject. For instance, when visually evaluating fluorescence images thatcapture transit of a dye bolus through tissue, clinicians' assessment ofblood flow and/or tissue perfusion is confounded by parameters (e.g.,brightness, image contrast, image noise) that are independent ofperfusion properties of the tissue. Additionally, clinicians' merevisual evaluation of the images is subjective and may vary fromclinician to clinician, patient to patient, and/or imaging session toimaging session. Furthermore, due to a clinician's lack of memory orinaccurate recollection of previous visual assessments, reliably andconsistently comparing and tracking blood flow and/or perfusion statusof a patient over time across multiple imaging sessions may bechallenging.

The methods and systems described herein for assessing tissue (e.g,healing of tissue) process and present data to the user in a manner thatenables more effective clinical decision making. For instance, the oneor more coefficient-derived image maps may be spatial maps thatconcisely shows relative differences between different regions oftissue, with respect to dynamic behavior of an imaging agent in thetissue. For example, the coefficient-derived image map may be avisualization of how different areas of the tissue vary in healingstatus, tissue property, and/or other tissue condition (e.g.,inflammation, malignancy, disease, other abnormality, or a combinationthereof, etc.) in a manner that is easily perceptible and identifiableby a human being. As described further herein, these quantifiedvisualizations reduce ambiguity and the effect of clinicians'subjectivity, by facilitating a standardized protocol for assessingblood flow and/or perfusion and/or assessing of tissue (e.g., healing).Thus, these quantified visualizations enable a clinician to make moreconsistent clinical assessments and/or medical treatment decisions.Furthermore, assessment of progress of healing and other assessments maybe derived, at least in some circumstances, from content of a singlecoefficient-derived image map where other imaging modalities (e.g.,color images visualizing the external surface of the tissue) fail toenable such assessments.

Although various exemplary embodiments are described in thespecification in the context of a time series of fluorescence images,the method may be applied to other sources of images generated as a timeseries which relate to a dynamic behavior of an imaging agent in thetissue and for other clinical purposes. For example, the images may bederived from computerized tomographic (CT) angiography with aradio-opaque contrast dye for blood flow and tissue perfusionassessment. As another example, the images may be derived from positronemission tomography (PET) using a fluorodeoxyglucose (FDG) or otherradiotracer to evaluate metabolic activity and potentially assesspathology and/or provide information usable for assessing pathology. Asanother example, the images may be derived from contrast-enhancedultrasound imaging employing the use of gas-filled microbubble contrastmedium administered intravenously to the systemic circulation. Suchultrasonic imaging using microbubble contrast agents enhances theultrasound backscatter or reflection of the ultrasound waves to producea unique sonogram with increased contrast due to the high echogenicity(i.e., ability of an object to reflect the ultrasound waves) differencebetween the gas in the microbubbles and the soft tissue.Contrast-enhanced ultrasound can be used, for example, to image bloodperfusion and blood flow in organs.

Generating the Time Series of Signal Intensity Data

As shown in FIG. 1, the method may include generating a time series ofsignal intensity data 110. The time series of signal intensity data ofthe tissue of the subject may include fluorescence images or video (ordata representative thereof) generated by fluorescence imagingtechnologies employing a fluorescence imaging agent such as, forexample, indocyanine green (ICG) dye as a fluorescence imaging agent.ICG, when administered to the subject, binds with blood proteins andcirculates with the blood in the tissue. Although reference is made inthe specification to a fluorescence agent or a fluorescence dye, othersuitable imaging agents may be used depending on the type of imagingtechnology being employed to generate the time series of signalintensity data.

In some variations, the fluorescence imaging agent (e.g., ICG) may beadministered to the subject as a bolus injection, in a suitableconcentration for imaging. In some variations where the method isperformed to assess tissue perfusion, the fluorescence imaging agent maybe administered to the subject by injection into a vein or artery of thesubject such that the dye bolus circulates in the vasculature andtraverses the microvasculature. In some variations in which multiplefluorescence imaging agents are used, such agents may be administeredsimultaneously (e.g., in a single bolus), or sequentially (e.g., inseparate boluses). In some variations, the fluorescence imaging agentmay be administered by a catheter. In some variations, the fluorescenceimaging agent may be administered to the subject less than an hour inadvance of performing the measurements for generating the time series offluorescence images. For example, the fluorescence imaging agent may beadministered to the subject less than 30 minutes in advance of themeasurements. In other variations, the fluorescence imaging agent may beadministered at least 30 seconds in advance of performing themeasurements. In some variations, the fluorescence imaging agent may beadministered contemporaneously with performing the measurements.

In some variations, the fluorescence imaging agent may be administeredin various concentrations to achieve a desired circulating concentrationin the blood. For example, in some variations for tissue perfusionassessment where the fluorescence imaging agent is ICG, the fluorescenceimaging agent may be administered at a concentration of about 2.5 mg/mLto achieve a circulating concentration of about 5 μM to about 10 μM inblood. In some variations, the upper concentration limit for theadministration of the fluorescence imaging agent is the concentration atwhich the fluorescence imaging agent becomes clinically toxic incirculating blood, and the lower concentration limit is the limit forinstruments used to acquire the time series of signal intensity datathat detect the fluorescence imaging agent circulating in blood. In somevariations, the upper concentration limit for the administration of thefluorescence imaging agent is the concentration at which thefluorescence imaging agent becomes self-quenching. For example, thecirculating concentration of ICG may range from about 2 μM to about 10mM.

Thus, in one aspect, the method may comprise administration of afluorescence imaging agent or other imaging agent to the subject, andgeneration or acquisition of the time series of fluorescence imagesprior to processing the image data. In another aspect, the method mayexclude any step of administering the fluorescence imaging agent orother imaging agent to the subject. For instance, the time series offluorescence images may be based on measurements of a fluorescenceimaging agent such as, for example, indocyanine green (ICG) dye that isalready present in the subject and/or based on autofluorescence response(e.g., native tissue autofluorescence or induced tissueautofluorescence), or measurements of a combination of autofluorescenceand exogenous fluorescence arising from a fluorescence imaging agent.

In some variations, a suitable fluorescence imaging agent is an agentwhich can circulate with the blood (e.g., a fluorescence dye which cancirculate with a component of the blood such as lipoproteins or serumplasma in the blood) and which fluoresces when exposed to appropriateexcitation light energy. The fluorescence imaging agent may comprise afluorescence dye, an analogue thereof, a derivative thereof, or acombination of these. A fluorescence dye may include any non-toxicfluorescence dye. In some variations, the fluorescence imaging agentoptimally emits fluorescence in the near-infrared spectrum. In somevariations, the fluorescence imaging agent is or comprises atricarbocyanine dye such as, for example, indocyanine green (ICG). Inother variations, the fluorescence imaging agent is or comprisesfluorescein isothiocyanate, rhodamine, phycoerythrin, phycocyanin,allophycocyanin, o-phthaldehyde, fluorescamine, rose Bengal, trypanblue, fluoro-gold, green fluorescence protein, flavins (e.g.,riboflavin, etc.), methylene blue, porphysomes, cyanine dyes (e.g.,cathepsin-activated Cy5 combined with a targeting ligand, Cy5.5, etc.),IRDye800CW, CLR 1502 combined with a targeting ligand, OTL38 combinedwith a targeting ligand, or a combination thereof, which is excitableusing excitation light wavelengths appropriate to each imaging agent. Insome variations, an analogue or a derivative of the fluorescence imagingagent may be used. For example, a fluorescence dye analogue or aderivative may include a fluorescence dye that has been chemicallymodified, but still retains its ability to fluoresce when exposed tolight energy of an appropriate wavelength. In variations in which someor all of the fluorescence is derived from autofluorescence, one or moreof the fluorophores giving rise to the autofluorescence may be anendogenous tissue fluorophore (e.g., collagen, elastin, NADH, etc.),5-aminolevulinic Acid (5-ALA), or a combination thereof.

In some variations, the fluorescence imaging agent may be provided as alyophilized powder, solid, or liquid. The fluorescence imaging agent maybe provided in a vial (e.g., a sterile vial), which may permitreconstitution to a suitable concentration by administering a sterilefluid with a sterile syringe. Reconstitution may be performed using anyappropriate carrier or diluent. For example, the fluorescence imagingagent may be reconstituted with an aqueous diluent immediately beforeadministration. Any diluent or carrier which will maintain thefluorescence imaging agent in solution may be used. As an example, ICGmay be reconstituted with water. In some variations, once thefluorescence imaging agent is reconstituted, it may be mixed withadditional diluents and carriers. In some variations, the fluorescenceimaging agent may be conjugated to another molecule, (e.g., a protein, apeptide, an amino acid, a synthetic polymer, or a sugar) so as toenhance solubility, stability, imaging properties or a combinationthereof. Additional buffering agents may optionally be added includingTris, HCl, NaOH, phosphate buffer, HEPES.

The time series of signal intensity data may comprise a plurality ofindividual image frames (e.g., fluorescence image frames), or datarepresentative of individual frames, ordered consecutively byacquisition time. For example, a time series of signal intensity datacan be acquired using an ICG-based fluorescence imaging system, wherethe subject receives an intravenous injection of ICG immediately priorto procedure, and the tissue is illuminated with light at ICG'sexcitation wavelengths while the resulting fluorescence emission fromthe dye as it transits the target tissue is imaged. The fluorescenceimages may subsequently be stored as a series of individual frames, orsignal intensity data representative of individual frames (e.g.,compressed video), ordered consecutively by their acquisition time.

In some variations, the individual image frames of the time series arespatially aligned or registered. For example, a typical time series offluorescence images may be recorded over 2 to 3 minutes, during whichsome subjects' movements may be unavoidable. As a result, the sameanatomical features can appear at different positions in image framesacquired at different times during the image time series acquisitionperiod. Since such misalignments can introduce errors in the subsequentanalysis where the level of fluorescence for each pixel or a group ofpixels is followed over time. To help reduce errors, the generated imageframes may be spatially aligned (registered) with each other. In somevariations, image registration or alignment refers to a process ofdetermining the spatial transform that maps points from one image tohomologous points in the second image.

Image registration may be an iterative process. For example, accordingto an exemplary embodiment, image registration may use one or more ofthe following set of components: two input images, a transform, ametric, an interpolator, and an optimizer. A transform maps the fixedimage space into the moving image space. An optimizer is required toexplore the parameter space Insight Segmentation and RegistrationToolkit (ITK) (http://itk.org/) based implementation of the transform insearch of optimal values of the metric may be used. The metric compareshow well the two images match each other. Finally, the interpolatorevaluates the intensities of the moving image at non-grid positions. Toalign the entire time series of fluorescence images, this procedure isexecuted for all the frames included in the analysis. The componentloops through the range of input series frames, subtracts a backgroundimage for baseline correction and applies noise-reduction filters, thenregisters consecutive pairs of images.

In some variations, the time series of fluorescence images ispre-processed to, for example, extract selected data, calculate abaseline intensity, perform an image quality improvement process, or acombination thereof.

Extraction of selected data may, for example, comprise cropping tolocate and exclude certain data from the image time series data. Forexample, during a fluorescence imaging procedure of the subject, anoperator might start recording the time series of fluorescence images(or signal intensity data) well before the fluorescence imaging agentreaches the target tissue As a result, the time series of fluorescenceimages might have a significant number of “dark” frames in thebeginning, thus adding unnecessary computational time for the framesthat contain no meaningful data. To mitigate the problem, cropping canbe used to remove those “dark” frames from the beginning of the timeseries of fluorescence images. In addition, when the subject is injectedwith the fluorescence imaging agent (e.g., ICG), the fluorescence signalfrom the imaging agent as it transits the target tissue typicallyproceeds through a series of phases: rapid increase of fluorescenceintensity as the imaging agent enters the tissue through arterialvessels, followed by a period of stable fluorescence as the imagingagent traverses the microvasculature, then slow decrease in fluorescenceintensity due to the venous outflow of the imaging agent, followed by aperiod of residual fluorescence as any imaging agent retained in thelining of the vasculature released into the bloodstream. This last“residual” phase can last for several minutes and, as it is not directlyindicative of blood flow, does not typically provide meaningfulperfusion information. Thus, cropping may be used to locate and excludethe residual phase from subsequent steps of analysis.

In some variations, pre-processing may include calculation of thebaseline intensity. For example, when the time series of fluorescenceimages is being generated by a fluorescence imaging system, variousexternal factors can contribute to the fluorescence of the recordedseries, such as camera noise, thermal noise, and/or presence of residualfluorescence dye from an earlier injection. In order to minimize theinfluence of such factors on the analysis, the baseline intensity may becalculated for every series, and the analysis of the data may beadjusted accordingly.

In some variations, pre-processing may include an image qualityvalidation process. Such a process may comprise a starting brightnesstest in embodiments where, for example, the acquisition of the timeseries of fluorescence images has started too late and the imaging agenthas already begun its transit of the target tissue by the time the firstframe was captured. In this scenario, the time series of fluorescenceimages cannot be reliably analyzed or processed since the informationrelating to the start of perfusion has been lost. As a result, suchseries data would be rejected.

In some variations, the image quality validation process may comprise abrightness change test. Such a test may be used, for example, ininstances where the fluorescence imaging system was suddenly movedduring the image acquisition, foreign objects appeared in the field ofview, or a light from an external source illuminated the scene while theseries was being captured. All of these events may significantly distortthe results of any subsequent analysis. Accordingly, the time series offluorescence images or signal intensity data subjected to such a testmight fail the validation procedure (be identified as being unsuitablefor further processing). According to an exemplary embodiment, thebrightness change test comprises a calculation of the difference betweenaverage intensities of neighboring frames in the time series offluorescence images and compares it to a selected intensity differencethreshold. In order to pass validation, the differences in intensitiesof all consecutive frames must be within the limit specified by theselected intensity difference threshold.

In some variations, the image quality validation process may comprise anintensity peak location test to check that the acquisition of the timeseries of fluorescence images has not been stopped prematurely. Forexample, the intensity peak location test ensures that a sufficientnumber of frames have been acquired to cover all phases of the dye bolustransit through the tissue. According to an exemplary embodiment, thefluorescence intensity peak location test comprises finding the framewith the maximum average fluorescence intensity and verifying that it isnot the last frame in the time series of fluorescence images. Shouldthis condition fail, it will be a strong indication that thefluorescence intensity values have not reached their maximum yet andsuch a time series of fluorescence images is not suitable for furtheranalysis.

In some variations, the image quality validation process may yet furthercomprise a maximum fluorescence intensity test. The purpose of the testis to filter out the time series of fluorescence images in which theimages are too dark (majority of pixels fall below a pre-definedthreshold) or over-saturated (majority of pixels are above a pre-definedsaturation threshold).

The curvature of the tissue surface, excessive movement during the imageacquisition procedure, dark or oversaturated images, foreign objectswithin imaged area and external light or shading can affect the qualityof the time series of fluorescence images, and thus the subsequentprocessing of such signal intensity data. To mitigate these problems, awell-structured imaging protocol and a fluorescence imaging systemdesigned to minimize such issues may be used.

The time series of signal intensity data or images may define aplurality of calculation regions. Each calculation region may be animage element such as, for example, a single pixel or group of pixels, avoxel or group of voxels, or some other spatially defined area or volumein the time series of fluorescence images. Each calculation region maybe identical in size to all other calculation regions, or may bedifferent in size compared to some or all other calculation regions. Inone variation, the boundaries and/or distribution of one or morecalculation regions may be pre-defined (e.g., a calculation region foreach pixel or voxel, or a calculation region for each 2×2 group ofpixels or 2×2×2 block of voxels). In another variation, the boundariesand/or distribution of one or more calculation regions may be defined bya user such as the clinician.

Determining Coefficient Values

As shown in FIG. 1, the method may include determining, for eachcalculation region, a coefficient value 114 that is related to at leasta portion of the time-intensity curve corresponding to the calculationregion. As shown schematically in FIGS. 2A and 2B, a giventime-intensity curve 212 (FIG. 2B) corresponding to a particularcalculation region 210 (FIG. 2A) describes the intensity of fluorescencesignal observed in that calculation region throughout the time series offluorescence signal intensity data. In some variations, a time-intensitycurve describes all phases (e.g. arterial, micro-vascular, venous andresidual in angiography applications), a subset of a phase or of acombination of phases, a subset of all phases, or a derivative thereof(including, for example, determinations based upon first and second timederivatives associated with changes in fluorescent intensity on apixel-by-pixel, or voxel-by-voxel, basis). All or some of thetime-intensity curves may be generated by a processor embodied in afluorescence imaging system that generated the fluorescence images, orby a processor remote from the fluorescence imaging system thatgenerated the fluorescence images.

In some variations, as shown in FIG. 2B, a time-intensity curve 212comprises a region of increasing intensity, a region of peak intensity,a plateau region, a region of decreasing intensity, or a combinationthereof. In the context of fluorescence imaging (e.g., fluorescenceangiography), as shown in FIG. 3, a time-intensity curve 312 mayrepresent the transit of a fluorescence imaging agent (e.g., afluorescence dye) bolus through the tissue as a series of phases: anarterial phase, a micro-vascular phase, a venous phase, a residualphase, or a combination thereof. The shape of the time-intensity curve(or a portion thereof), an area under the time-intensity curve, or acombination thereof may be indicative of distribution of thefluorescence imaging agent in the tissue of the subject, blood flow inthe tissue, or a combination thereof. In some applications, thedistribution of the imaging agent in the tissue of the subjectrepresents a property of the tissue, a condition of the tissue (e.g.,inflammation, malignancy, abnormality, disease) or a combinationthereof.

In some variations, the coefficient values for the calculation regionsmay characterize a shape of at least a portion of the time-intensitycurve. For instance, a coefficient value may characterize a region ofincreasing slope of the time-intensity curve (e.g., arterial phase ofthe time-intensity curve, or a region correlating to a time periodbetween a start time of measurement of the transit of the imaging agentthrough the tissue and time of maximum signal intensity, etc.), a regionof decreasing slope of the time-intensity curve (e.g., a venous phase ofthe time-intensity curve, or a region correlating to a time periodbetween a time of maximum signal intensity and an end time ofmeasurement of the transit of the imaging agent through the tissue), ora combination thereof.

In some variations, the coefficient values are related to a mathematicalmodel which approximates a signal intensity arising from the imagingagent that circulates with blood and transits vasculature of the tissueas a function of time. In one exemplary embodiment relating tofluorescence imaging using, for example, ICG as the imaging agent, thecoefficient values may be related to, for example, the mathematicalmodel in Formula 1 disclosed in Eren et al. in Assessment ofMicrocirculation of an Axial Skin Flap Using Indocyanine GreenFluorescence Angiography, Plastic and Reconstructive Surgery, December1995, pp. 1636 to 1649 (hereinafter referred to as “Eren”), which isincorporated herein by reference. One skilled in the art will appreciatethat the mathematical model described in connection with Formula 1 isexemplary only, and may be further modified to approximate the transitof the imaging agent in the tissue, or replaced by a differentfunctionally-equivalent mathematical model.

$\begin{matrix}{{f(t)} = {{f_{{Ma}\; x}( {1 - ^{- \frac{t^{\prime}}{C_{Inf}}}} )}^{- \frac{t^{\prime}}{C_{Eff}}}}} & {{Formula}\mspace{14mu} 1}\end{matrix}$

-   -   where        -   f_(Max)=maximum intensity;        -   t′=t−t_(Lag);        -   t_(Lag)=influx lag time (the time it takes for the dye to            arrive from the site of bolus injection to the region of            interest);        -   C_(Inf)=influx (arterial) coefficient or time constant; and        -   C_(Eff)=efflux (venous) coefficient or time constant.

Although Eren postulated the mathematical model of Formula 1, such amodel was merely taught in Eren to generate numerical and histogram datarelating to the influx and efflux coefficients or time constants. Thedata reported by Eren in its various tables or histograms is largelydevoid of any clinically-meaningful insights. In particular, Eren failedto suggest or appreciate, based on the generated data, that the datacould itself be further utilized or transformed for purposes ofgenerating a new image of the tissue (e.g., an arterialcoefficient-derived image map and/or a venous coefficient-derived imagemap of the tissue), and that such new coefficient-derived image of thetissue, if so generated, would provide the user with meaningful visualand quantitative insight into the healing of the tissue (e.g., visualinsight as to the pattern of changes in the wound and the wound healingprocess, and quantitative insight based on the change in the areas ofthe visual pattern over time). Eren further failed to appreciate thateach of such new coefficient-derived images, (e.g., the arterialcoefficient-derived image and the venous coefficient-derived image), andin particular the patterns in such images, alone or in a synergisticcombination provide particular qualitative and quantitative insight intopredicting the potential for healing of the wound tissue. For example,Eren failed to appreciate that the venous coefficient-derived imagealone is highly specific in its predictive and diagnostic value althoughcan vary based on a particular clinical application. Similarly, Erenfailed to recognize the clinical diagnostic and predictive value ofgenerating spatial maps or images of the tissue based upon suchcoefficient-derived values, which uniquely facilitate visualization ofthe dynamic perfusion patterns associated with the wound healing process(e.g., comparing the relative size and shape of the spatially-mappedareas corresponding to the venous coefficient-derived image and thearterial coefficient-derived image, as well as their mutual positionswith respect to one another over time, which provide both qualitativeand quantitative indications as to the relative status and extent of thewound healing process). Eren further failed to suggest or appreciatethat such coefficient-derived maps or images, such as the venouscoefficient-derived map or image, could be used to visualize a vesselnetwork and discriminate between different vessels in the network.

In accordance with the exemplary method utilizing Formula 1, acoefficient value (e.g., C_(Inf), C_(Eff)) is calculated at one or morepoints on the tissue (e.g., for an image element such as, for example, apixel or a group of pixels) using empirical signal intensity data forthe imaging agent in the tissue, where the empirical signal intensitydata comprises a set of intensity values over time. According to anembodiment, calculation of C_(Inf) may be performed using Formula 2.

g(t)=log [f(t)−fmax] where t<tmax  Formula 2

Following the calculation of the logarithm of the data, linearregression may be used to derive a straight line, where the slope of thestraight line provides the influx (arterial) coefficient value. Theefflux (venous) coefficient value is similarly obtained, but without thesubtraction of fmax.

In some variations, empirical signal intensity data may include datafrom empirical sources such as, for example, purely experimental and/orclinical data, data derived from purely experimental/clinical data, or acombination thereof. According to an embodiment, in the mathematicalmodel represented by Formula 1, where the coefficient approximates theshape of the time-intensity curve, C_(Inf) represents the region ofincreasing slope of the time-intensity curve, and C_(Eff) represents theregion of decreasing slope of the time-intensity curve.

Converting the Coefficient Values into a Coefficient-Derived Image Map

As shown in FIG. 1, the method may include converting the coefficientvalues 116 across the plurality of calculation regions into acoefficient-derived image map. The resulting coefficient-derived imagemap visualizes the differences in the dynamic behavior of the imagingagent among different regions of the tissue of the subject, and furthermay provide a visual representation of external and/or internaltopography of the tissue. Thus, the coefficient-derived image map mayhighlight different characteristics of the tissue in an objective,easily understood manner, and may represent a qualitative profile of thetissue. As further described above, as a result, the coefficient-derivedimage map may facilitate assessment of healing of the tissue (e.g.,progress of healing, efficacy of clinical intervention, etc.).

Converting the coefficient values 116 into a coefficient-derived imagemap may include correlating each coefficient value to an intensityvalue, such that the calculation regions in the coefficient-derivedimage map may be depicted with varying intensity values corresponding tothe coefficient values. The conversion may involve assigning a displaybrightness value to each coefficient value wherein the coefficient valueand brightness value are in a direct relationship (e.g., the higher thecoefficient value, the higher the pixel's intensity). The directrelationship may be linear or nonlinear. In other variations, theconversion may be based on an indirect relationship between thecoefficient value and brightness value.

In some variations, the coefficient value may be mapped to a gray scaleor a color scale value. For example, the coefficient values may bemapped to an 8-bit grayscale display value (e.g., from 0 to 255),allowing for a grayscale image representation of the coefficient values.In some variations, to optimize visual perception, a color scheme can beapplied to the grayscale image representation with different grayscalevalue ranges represented in appropriately contrasting colors (such as afalse color or pseudo color). Other scales may additionally oralternatively be applied to convert the coefficient values into pixelvalues for the coefficient-derived image map, such that the differencesin pixel values reflect the relative differences in coefficient valuesand among different regions of the imaged tissue.

In another variation, the arterial and venous coefficients may bemathematically pair-wise combined to produce a plurality of combinedcoefficients, and the combined coefficients may be converted into acoefficient-derived image map. By way of example, one or more combinedcoefficients may be derived by a weighting of the relative contributionsof each of the arterial and venous coefficients, deriving a differentialbetween the arterial and venous coefficients, and/or a summing of thearterial and venous coefficients, etc.

In some variations, the method may include generating at least twocoefficient-derived image maps. For instance, as shown in FIG. 4, themethod may include determining, for each calculation region, a secondcoefficient value 414 that is related to at least a second portion ofthe time-intensity curve corresponding to the calculation region, andconverting the second coefficient values 416 across the plurality ofcalculation regions into a second coefficient-derived image map. Forexample, the first coefficient-derived image map may be an arterialcoefficient-derived image map (e.g., based on C_(Inf) coefficient valuesusing Formulas 1 and 2) and the second coefficient-derived image map maybe a venous coefficient-derived image map (e.g., based on C_(Eff)coefficient values using Formulas 1 and 2).

Assessing Tissue of the Subject Based at Least in Part on theCoefficient-Derived Image Map

It was surprisingly found, based on animal and human data, that theprocess of spatially-mapping the coefficient values to an image mapprovides a highly useful qualitative and/or a quantitative predictor ofthe tissue's healing potential and healing state. The external andinternal topography features in the coefficient-derived image of thetissue facilitate enhancement and identification of features of thetissue that may not be apparent or visible from a white light imageand/or maximum perfusion image of the target anatomy or a numericalrepresentation of the coefficient data relating to the tissue, as isfurther described below in the examples.

As shown in FIG. 1, the method for assessing healing of tissue of asubject may include assessing tissue of the subject based at least inpart on the coefficient-derived image map 120. For instance, thecoefficient-derived image may be used alone to assess the tissue, or incombination with the quantitative predictor described below, or yetfurther in combination with another image (e.g., overlaid with ananatomical image) or other data relating, for example, to a systemic orlocal condition of the subject providing a particular clinical contextfor that subject. Such a condition may comprise a comorbid conditionincluding, for example, hypertension, dyslipidemia, diabetes mellitus,chronic obstructive pulmonary disease, coronary artery disease, chronickidney disease, or a combination thereof. Furthermore, thecoefficient-derived image (e.g., venous coefficient-derived image) mayfacilitate visualization of a vessel and/or vessel network.

Assessing Based on Heterogeneous Pattern

In some variations, assessing tissue of the subject may compriseassessing a healing status of the wound based on a heterogeneous visualpattern in a single coefficient-derived image 122, where theheterogeneous pattern is indicative of an actual or suspected wound. Theheterogeneous pattern may manifest as a result of a difference in thecoefficients in the coefficient-derived images, which correlates with adifference in a dynamic behavior of an imaging agent (e.g., ICG, etc.)in the tissue. Such information from a single coefficient-derived image(e.g., venous coefficient-derived image) may facilitate providing aprognosis for the health of the tissue, as the nature of the pattern mayprovide insight into the healing potential or stage of the actual orsuspected wound tissue, without requiring the analysis of multiple imagetaken over time in order to determine current healing potential.

For instance, as further discussed in the examples below, in a healthyorganism, the wound healing process manifests itself in a temporalprogression of distinct patterns in a coefficient-derived image (e.g.,venous coefficient-derived image). By identifying these patterns, onecan both assess the severity of the damage in the tissue and predict thehealing potential of the tissue (e.g., wound). Various stages of thewound healing process are exemplified in the schematics of FIGS. 5A-5D.An early stage of the healing process may comprise an increased effluxor venous activity inside or around the wound, which is schematicallyillustrated in FIG. 5A generally as a partial ring or crescent 520 aaround or embracing the wound 510 (e.g., Stage 1). This pattern 520 a iscorrelated with the highest degree of tissue damage and is the furthestaway from healing. As shown in the schematic of FIG. 5B, as the healingprogresses, the partial ring transforms into a complete ring pattern 520b surrounding the wound 510 (e.g., Stage 2). The complete ring pattern520 b is still indicative of a severe compromise to the tissueperfusion, but is an improvement in healing status compared to thepartial ring pattern 520 a illustrated in FIG. 5A. As shown in FIG. 5C,the complete ring pattern transforms into a “filled circle” pattern 520c (e.g., Stage 3) substantially overlaid with the wound 510 as thehealing process continues. The “filled circle” pattern 520 c appears tobe the most common reaction to a relatively minor perfusion compromiseand is associated with an active state of healing. As the healingprocess further continues, the pattern eventually transforms into the“collapsed circle” pattern 520 d (e.g., Stage 4) lying within theoriginal wound region 510, as shown in FIG. 5D.

Although a single coefficient-derived image may be sufficient todetermine or predict current healing potential, in some variations, themethod may further include comparing a plurality of coefficient-derivedimage maps that are based on a plurality of time series of signalintensity data captured over time, and assessing, for example, progressof healing of tissue, efficacy of clinical intervention, or acombination thereof based on the comparison of the plurality ofcoefficient-derived image maps.

Assessing Based on a Quantitative Predictor

In some variations, as shown in FIG. 1, assessing tissue of the subjectmay include generating a quantitative predictor based on at least aportion of the coefficient-derived image 124. For instance, thequantitative predictor may quantify particular characteristics of asingle coefficient-derived image, such as area of abnormal activity(e.g., number of pixels or voxels of a region of abnormal activity),eccentricity of abnormal activity (e.g., to indicate how different froma “filled circle” pattern the current pattern is), etc.

In some variations in which the first and second coefficient-derivedimage maps (e.g., arterial and venous coefficient-derived image maps)have been generated as shown in FIG. 4, assessing tissue of the subject420 may include generating a quantitative predictor based on the firstand second coefficient-derived image maps. Based on the in vivopre-clinical data (described below in the examples), it appears that theinitial increase in efflux venous activity is followed by an increase ofthe influx arterial activity. In contrast to the range of differentpatterns exhibited during the healing process and evident in the venouscoefficient-derived image map, the arterial coefficient-derived imageshows predominantly one pattern during all stages of the wound healingprogress, namely the filled circle pattern (similar to FIG. 5C). Thearea and intensity of the filled circle pattern in the arterialcoefficient-derived image map may change from one stage to another, butits shape generally may remain unchanged. It further appears that thereis a phase shift between the formation of the influx and effluxpatterns. Namely, throughout the healing process, the efflux patternsare first to appear and first to dissipate into a normal pattern, whilethe influx circle pattern forms some time later in the process, andlingers after the efflux pattern has disappeared. Accordingly, thequantitative predictor of the progress of healing of the tissue may, forexample, be based on a comparison of the arterial and venous coefficientimage maps and thus serve as an objective characterization of a state orprogress of healing of the tissue.

For example, the quantitative predictor may be based on a ratio or othercomparative metric of the relative sizes of selected regions in thefirst and second coefficient-derived image maps. For instance, the areaor volume of a region of abnormal activity in the venouscoefficient-derived image map can be measured (e.g., number of pixels orvoxels) and compared to the measured area of volume of a region ofabnormal activity in the arterial coefficient-derived image map (e.g.,dividing an area of a first selected region of the venouscoefficient-derived image map by the area of a second selected region ofthe arterial coefficient-derived image map). The quantitative predictormay thus numerically characterize a state or progress of healing. Forexample, when both venous and arterial areas of increased activity inthe respective coefficient-derived images cover about the same parts oftissue in a “filled circle” pattern, a ratiometric quantitativepredictor may be equal to about 1, indicating that the wound is in itsongoing process of healing. Furthermore, such a quantitative predictormay additionally or alternatively be used to provide prognosticinformation of wound healing, such as whether to stop or continuetreatment.

Although a single quantitative predictor obtained for a subject at oneparticular time (e.g., a single clinical session) may be sufficient todetermine or predict current healing potential, in some variations, themethod may further include tracking a change in the quantitativepredictor over time. For instance, a change in the quantitativepredictor may be represented in a graph form which facilitates derivinginformation about the rate and slope. A graphical representation of thequantitative predictor over time may facilitate an evaluation of achange in the quantitative predictor over time, which is indicative, forexample, of a change in a state or healing progress of the wound overtime.

In some variations, the quantitative predictor may be correlated with arisk estimate for clinically relevant (e.g., perfusion-related)condition. Such assessments may be made pre-intervention, duringtreatment/procedure, and post-intervention. The method may also furthercomprise defining a diagnosis to identify and characterize a clinicallyrelevant (e.g., perfusion-related) condition in the subjectpre-intervention, during treatment/procedure, and post-intervention.Alternatively, the method may omit such correlation and/or diagnosis.

In various further embodiments, the coefficient values, thecoefficient-derived images, and/or the quantitative predictor(s) may beused as input into a machine learning process (i.e., getting a processoror a computer to act without being explicitly programmed), deep machinelearning, data mining, and/or pattern recognition where the machinelearning is then subsequently used for assessment of a time series ofsignal intensity data or an image of tissue.

Displaying the Coefficient-Derived Image Map on a Display

In some variations, as shown in FIG. 1, the method may further includedisplaying one or more coefficient-derived image maps on a display 118.For example, the coefficient-derived image map may be displayed within auser interface on a video monitor in a fluorescence imaging system, orother suitable display. The coefficient-derived image map may bedisplayed alone, or in combination with another image (e.g., overlaidwith or superimposed on an anatomical image) or other data. Such otherdata may relate, for example, to a systemic or local condition of thesubject providing a particular clinical context for that subject. Such acondition may comprise a comorbid condition including, for example,hypertension, dyslipidemia, diabetes mellitus, chronic obstructivepulmonary disease, coronary artery disease, chronic kidney disease, or acombination thereof. Furthermore, the coefficient-derived image map mayadditionally or alternatively be displayed in combination with thequantitative predictor described above. In some variations, thecoefficient-derived image map may be displayed with a ranking map imageand/or a wound index value characterizing a wound in the tissue such asthose described in U.S. patent application Ser. No. 15/013,945, filedFeb. 2, 2016 and entitled “METHODS AND SYSTEMS FOR CHARACTERIZING TISSUEOF A SUBJECT,” which is hereby incorporated in its entirety by thisreference.

Systems for Assessing Tissue (e.g., Healing of Tissue)

A system for assessing tissue (e.g., healing of tissue) includes one ormore processors and memory having instructions stored thereon, whereinthe instructions when executed by the one or more processors cause thesystem to perform the methods substantially as described above forassessing healing of tissue.

In some variations, the system for assessing or characterizing tissue ofa subject is a fluorescence imaging system. FIG. 6 is a schematicexample of a fluorescence imaging system 610. The fluorescence imagingsystem 610 comprises a light source 612 to illuminate the tissue of thesubject to induce fluorescence emission from a fluorescence imagingagent 614 in the tissue of the subject (e.g., in blood), an imageacquisition assembly 616 configured to generate the time series offluorescence signal intensity data from the fluorescence emission, and aprocessor assembly 618 configured to process the generated time seriesof signal intensity data according to any of the variations of themethods described herein. The processor assembly 618 may include memory668 with instructions thereon, a processor module 662 configured toexecute the instructions on memory 668 to process the time series ofsignal intensity data as described in connection with the variousembodiments of the methods described above, and a data storage module664 to store the unprocessed and/or processed time series of signalintensity data. In some variations, the memory 668 and data storagemodule 664 may be embodied in the same storage medium, while in othervariations the memory 668 and the data storage module 664 may beembodied in different storage mediums. The system may further include adisplay 666 on which to display images and other data, such as some orall of the time series of fluorescence images representing the signalintensity data or other input data, a quantitative predictor, a rankingmap image, and/or a wound index value.

In some variations, the light source 612 includes, for example, anillumination module 620. Illumination module 620 may include afluorescence excitation source configured to generate an excitationlight having a suitable intensity and a suitable wavelength for excitingthe fluorescence imaging agent 614. As shown in FIG. 7, the illuminationmodule 620 may comprise a laser diode 722 (e.g., which may comprise, forexample, one or more fiber-coupled diode lasers) configured to providean excitation light to excite the fluorescence imaging agent (not shown)in tissue of the subject. Examples of other sources of the excitationlight which may be used in various embodiments include one or more LEDs,arc lamps, or other illuminant technologies of sufficient intensity andappropriate wavelength to excite the fluorescence imaging agent in thetissue. For example, excitation of the fluorescence imaging agent inblood, wherein the fluorescence imaging agent is a fluorescence dye withnear infra-red excitation and emission characteristics, may be performedusing one or more 793 nm, conduction-cooled, single bar, fiber-coupledlaser diode modules from DILAS Diode Laser Co, Germany.

Referring again to FIG. 6, in some variations, the light output from thelight source 612 may be projected through one or more optical elementsto shape and guide the output being used to illuminate the tissue areaof interest. The optical elements may include one or more lenses, lightguides, and/or diffractive elements so as to ensure a flat field oversubstantially the entire field of view of the image acquisition assembly616. The fluorescence excitation source may be selected to emit at awavelength close to the absorption maximum of the fluorescence imagingagent 614 (e.g., ICG, etc.). For example, as shown in FIG. 7, the output724 from the laser diode 722 may be passed through one or more focusinglenses 726, and then through a homogenizing light pipe 728 such as, forexample, light pipes commonly available from Newport Corporation, USA.Finally, the light may be passed through an optical diffractive element732 (i.e., one or more optical diffusers) such as, for example, groundglass diffractive elements also available from Newport Corporation, USA.Power to the laser diode 722 may be provided by, for example, ahigh-current laser driver such as those available from Lumina Power Inc.USA. The laser may optionally be operated in a pulsed mode during theimage acquisition process. An optical sensor such as a solid statephotodiode 730 may be incorporated into the illumination module 620 andmay sample the illumination intensity produced by the illuminationmodule 620 via scattered or diffuse reflections from the various opticalelements. In some variations, additional illumination sources may beused to provide guidance when aligning and positioning the module overthe area of interest.

Referring again to FIG. 6, in some variations, the image acquisitionassembly 616 may be a component of a fluorescence imaging system 610configured to acquire the time series of signal intensity data from thefluorescence emission from the fluorescence imaging agent 614. The imageacquisition assembly 616 may include a camera module 640. As shown inFIG. 8, the camera module 640 may acquire images of the fluorescenceemission 842 from the fluorescence imaging agent in the tissue by usinga system of imaging optics (e.g., 846 a, 846 b, 848 and 850) to collectand focus the fluorescence emission onto an image sensor assembly 844.The image sensor assembly 844 may comprise at least one 2D solid stateimage sensor. The solid state image sensor may be a charge coupleddevice (CCD), a CMOS sensor, a CID or similar 2D sensor technology. Thecharge that results from the optical signal transduced by the imagesensor assembly 844 is converted to an electrical video signal, whichincludes both digital and analog video signals, by the appropriateread-out and amplification electronics in the camera module 640.

According to an exemplary embodiment of a fluorescent imaging system,the light source may provide an excitation wavelength of about 800nm+/−10 nm, and the image acquisition assembly uses emission wavelengthsof at least 820 nm with NIR-compatible optics for, for example, ICGfluorescence imaging. In an exemplary embodiment, the NIR-compatibleoptics may include a CCD monochrome image sensor having a GigE standardinterface and a lens that is compatible with the sensor with respect tooptical format and mount format (e.g., C/CS mount).

In some variations, the processor module 662 comprises any computer orcomputing means such as, for example, a tablet, laptop, desktop,networked computer, or dedicated standalone microprocessor. Forinstance, the processor module 662 may include one or more centralprocessing units (CPU). In an exemplary embodiment, the processor module662 is a quad-core, 2.5 GHz processor with four CPUs where each CPU is amicroprocessor such as a 64-bit microprocessor (e.g., marketed as INTELCore i3, i5, or i7, or in the AMD Core FX series). However, in otherembodiments, the processor module 662 may be any suitable processor withany suitable number of CPUs and/or other suitable clock speed.

Inputs for the processor module 662 may be taken, for example, from theimage sensor 844 of the camera module 640 shown in FIG. 8, from thesolid state photodiode 730 in the illumination module 620 in FIG. 7,and/or from any external control hardware such as a footswitch orremote-control. Output is provided to the laser diode driver and opticalalignment aids. As shown in FIG. 6, in some variations, the processorassembly 618 may have a data storage module 664 with the capability tosave the time series of images, or data representative thereof, or otherinput data to a tangible non-transitory computer readable medium suchas, for example, internal memory (e.g. a hard disk or flash memory), soas to enable recording and processing of acquired data. In somevariations, the processor module 662 may have an internal clock toenable control of the various elements and ensure correct timing ofillumination and sensor shutters. In some variations, the processormodule 662 may also provide user input and graphical display of outputs.The fluorescence imaging system may optionally be configured with avideo display 666 or other monitor to display the time series offluorescence images as they are being acquired or played back afterrecording. The video display 666 may additionally or alternativelyvisualize data generated during performance of the methods describedherein, such as a coefficient-derived image map, quantitative predictor,ranking map image and/or wound index value.

In operation of the exemplary system described in FIGS. 6-8, the subjectis positioned relative to fluorescence imaging system 610 such that anarea of interest (e.g., target tissue region) is located beneath thelight source 612 and the image acquisition assembly 616 such that theillumination module 620 of light source 612 produces a substantiallyuniform field of illumination across substantially the entire area ofinterest. In some variations, prior to the administration of thefluorescence imaging agent 614 to the subject, an image may be acquiredof the area of interest for the purposes of background deduction. Toacquire fluorescence images, the operator of the fluorescence imagingsystem 610 may initiate the acquisition of the time series offluorescence images by depressing a remote switch or foot-control, orvia a keyboard (not shown) connected to the processor assembly 618. As aresult, the light source 612 is turned on and the processor assembly 618begins recording the fluorescence image data provided by the imageacquisition assembly 616. When operating in the pulsed mode of theembodiment, the image sensor 844 in the camera module 640 issynchronized to collect fluorescence emission following the laser pulseproduced by the diode laser 722 in the illumination module 620. In thisway, maximum fluorescence emission intensity is recorded, andsignal-to-noise ratio is optimized. In this embodiment, the fluorescenceimaging agent 614 is administered to the subject and delivered to thearea of interest via arterial flow. Acquisition of the time series offluorescence images is initiated, for example, shortly afteradministration of the fluorescence imaging agent 614, and the timeseries of fluorescence images from substantially the entire area ofinterest is acquired throughout the ingress of the fluorescence imagingagent 614. The fluorescence emission from the region of interest iscollected by the collection optics of the camera module 640. Residualambient and reflected excitation light is attenuated by subsequentoptical elements (e.g., optical element 850 in FIG. 8 which may be afilter) in the camera module 640 so that the fluorescence emission canbe acquired by the image sensor assembly 844 with minimal interferenceby light from other sources.

In some variations, following the acquisition or generation of the timeseries of fluorescence images, the processor assembly 618 (e.g.,processor module 662 or other processor) may then be initiated toexecute instructions stored on memory 668 and perform one or moremethods as described herein. The system 610 may visualize on display 666the ranking map and/or any clinical correlations or diagnosis derivedtherefrom or both may be displayed to the user as, for example, agrayscale or false color image, and/or stored for subsequent use.Additionally or alternatively, the system 610 may display on display 666a quantitative predictor.

In some variations, the system for assessing healing of tissue comprisesa user interface, a processor configured to communicate with the userinterface, and a non-transitory computer-readable storage medium havinginstructions stored which, when executed by the processor, cause theprocessor to perform one or more of the methods for assessing healing oftissue described herein. In some variations, the processor may be acomponent of the imaging system. In other variations, the processor maybe located remotely from and in communication with an imaging system,where the imaging system may be the fluorescence imaging systemdescribed above, or any suitable imaging system.

A tangible non-transitory computer readable medium havingcomputer-executable (readable) program code embedded thereon may provideinstructions for causing one or more processors to, when executing theinstructions, perform one or more of the methods for assessing healingof tissue described herein. Program code can be written in anyappropriate programming language and delivered to the processor in manyforms, including, for example, but not limited to informationpermanently stored on non-writeable storage media (e.g., read-onlymemory devices such as ROMs, CD-ROM disks, etc.), information alterablystored on writeable storage media (e.g., hard drives or the like),information conveyed to the processor through communication media, suchas a local area network, a public network such as the Internet, or anytype of media suitable for storing electronic instruction. When carryingcomputer readable instructions that implement the various embodiments ofthe method of the present invention, such computer readable mediarepresent examples of various embodiments of the present invention. Invarious embodiments, the tangible non-transitory computer readablemedium comprises all computer-readable media, and the present inventionscope is limited to computer readable media wherein the media is bothtangible and non-transitory.

A kit may include any part of the systems described herein and thefluorescence imaging agent such as, for example, a fluorescence dye suchas ICG or any suitable fluorescence imaging agent. In further aspects, akit may include a tangible non-transitory computer readable mediumhaving computer-executable (readable) program code embedded thereon thatmay provide instructions for causing one or more processors, whenexecuting the instructions, to perform one or more of the methods forassessing healing of tissue described herein. The kit may includeinstructions for use of at least some of its components (e.g., for usingthe fluorescence imaging agent, for installing the computer-executable(readable) program code with instructions embedded thereon, etc.). Inyet further aspects, there is provided a fluorescence imaging agent suchas, for example, a fluorescence dye for use in in the methods andsystems described herein.

EXAMPLES

In some of the examples described below, “color image” refers to animage obtained under ambient lighting conditions. Additionally, “maximumperfusion image” refers to a map created by assigning each pixel in thecalculation region of the time series of signal intensity data the valueof its maximum intensity reached during the entire measurement period.Furthermore, “arterial coefficient-derived image” and “venouscoefficient-derived image” refer to an image generated from C_(Inf)coefficients and C_(Eff), respectively, using the exemplary model ofFormula 1 described above.

A. Pre-Clinical In Vivo Data Pressure-Induced Wound ExperimentalProtocol

The protocol to induce the formation of pressure-induced wounds inrodents has been described in Stadler I, Zhang R Y, Oskoui P, WhittakerM S, Lanzafame R J, Development of a simple, noninvasive, clinicallyrelevant model of pressure ulcers in the mouse, J. Invest. Sung. 2004July-August, 17(4): 221-227, and Nunan R, Harding K G, Martin P,Clinical Challenges of Chronic Wounds: Searching for an Optimal AnimalModel to Recapitulate their Complexity, Disease Models & Mechanisms(2014) 7, 1205-1213.

White Wistar Rats (n=10) were anesthetized with about 2-4% isofluraneand the dorsal hair was removed. The dorsal skin of the back over theshoulder blades was gently pulled and placed between two disc-shapedmagnetic plates (5 mm diameter, 2.4 g weight), which created a 5 mmskinfold between the magnets. Magnets remained in position to induce thewound for about 1.0 h for 3 days in an animal group consisting of 3animals, and about 3.0 h for 3 days in an animal group consisting of 5animals. This procedure was used to induce ischemic areas of variableseverity. Animals remained anesthetized during this time. Isoflurane wasturned down to the lowest concentration that still rendered the animalunconscious (about 1-2%). If respiratory depression was noted (i.e.,respirations lower than 50-60 per minute, gasping, decreased depth ofrespirations, expired CO₂ increasing), then the animals were intubatedand ventilated using a Harvard rodent-specific ventilator for theduration of the procedure. Animals were given all the supportive carerequired for the anesthetic session including: heat (animals were kepton a circulating hot water blanket with temperature maintained above 35°C., fluids (animals were given 5 ml of warmed Lactated Ringers Solution(LRS) subcutaneously before and after the procedure), pain medications(animals were given an injection of ketoprofen at 5 mg/kg prior to theprocedure), and (D) eye lubrication (animals' eyes were protectedthroughout the procedure with Lacrilube®). Animals were recovered in aheated clean cage until ambulatory. Monitoring commenced for theremainder of the afternoon and evening to ensure signs of pain orirritation were not noted. Ketoprofen at the dose above was given atleast one more time at 24 h post-procedure. Assessment of bloodperfusion in tissue and visual inspection of the ischemic regions wereperformed at about 3 hours, 24 hours, and 48 hours after the removal ofthe magnetic plates.

Example 1 No Wound

Results were obtained for a control animal that did not develop anywound after removal of the magnetic plates described above. FIGS. 9A-9Dillustrate results for the control animal after 24 hours followingremoval of the magnetic plates, while FIGS. 10A-10D illustrate resultsfor the control animal after 48 hours following removal of the magneticplates. The color images (FIGS. 9A and 10A), maximum intensity images(FIGS. 9B and 10B), arterial coefficient-derived images (FIGS. 9C and10C), and venous coefficient-derived images (FIGS. 9D and 10D) do notdepict any visible abnormalities.

Example 2 Minor Pressure-Induced Wound

Results were obtained for an animal with a minor pressure-induced woundresulting from application of the magnetic plates described above. Theresults in FIGS. 11-13 illustrate the healing progression of the minorpressure-induced wound, which healed almost completely withoutintervention after about 48 h, as depicted in a series of color images,maximum perfusion images, arterial coefficient-derived images, andvenous coefficient-derived images. In particular, FIGS. 11A-11Dillustrate results for the animal with the minor wound after 3 hoursfollowing removal of the magnetic plates, FIGS. 12A-12D illustrateresults for the animal with the minor wound after 24 hours followingremoval of the magnetic plates, and FIGS. 13A-13D illustrate results forthe animal with the minor wound after 48 hours following removal of themagnetic plates.

After 3 hours, as shown in the color image of FIG. 11A, visible rednesson the skin surface of the animal can be seen. This visible rednesscorresponds to increased activity in the maximum perfusion image (FIG.11B), the arterial coefficient-derived image (FIG. 11C), and venouscoefficient-derived image (FIG. 11D). Furthermore, the venouscoefficient-derived image of FIG. 11D shows a “filled circles” patternof abnormally high activity. The arterial coefficient-derived image ofFIG. 11C also demonstrates increased activity in the wounded tissue, butthe pattern is still in its early stages of formation (scatteredhigh-activity area, as contrasted with the smoothly “filled” circles ofthe venous coefficient-derived image of FIG. 11D).

After 24 hours, as shown in the color image of FIG. 12A, moderatedecrease in the area of visible skin redness is apparent, which againcorresponds to increased activity in the maximum perfusion image (FIG.12B), the arterial coefficient-derived image (FIG. 12C), and venouscoefficient-derived image (FIG. 12D). Moreover, the venouscoefficient-derived image of FIG. 12D continues to show a “filledcircles” pattern, but the pattern of highest activity now appears to beconcentrated toward the center of the wound and the area of the abnormalvenous activity has decreased as well. The arterial coefficient-derivedimage of FIG. 12C shows high activity covering each wound circle.

After 48 hours, almost no abnormalities are apparent in the color image(FIG. 13A), maximum perfusion image (FIG. 13B), arterialcoefficient-derived image (FIG. 13C), and venous coefficient-derivedimage (FIG. 13D). Although some increased activity is shown in thearterial coefficient-derived image of FIG. 13C (as shown by the arrow),the wounds are on the verge of being fully healed.

Example 3 Severe Pressure-Induced Wound

Results were obtained for an animal with a severe pressure-induced woundresulting from application of the magnetic plates described above. Theresults in FIGS. 14-19 illustrate the healing progression of the severepressure-induced wound as depicted in a series of color images, maximumperfusion images, arterial coefficient-derived images, and venouscoefficient-derived images. In particular, FIGS. 14A-14D illustrateresults for the animal with the severe wound immediately followingremoval of the magnetic plates, FIGS. 15A-15D illustrate results for theanimal with the severe wound after 2 hours following removal of themagnetic plates, FIGS. 16A-16D illustrate results for the animal withthe severe wound after 24 hours following removal of the magneticplates, FIGS. 17A-17D illustrate results for the animal with the severewound after 48 hours following removal of the magnetic plates, FIGS.18A-18D illustrate results for the animal with the severe wound after 72hours following removal of the magnetic plates, and FIGS. 19A-19Dillustrate results for the animal with the severe wound after 8 daysfollowing removal of the magnetic plates.

Immediately after the removal of the magnetic plates, as shown in thecolor image of FIG. 14A, deep, red indentations are visible on thesurface of the skin. Additionally, there is a total absence of botharterial and venous activity in the wounds, as evidenced by theappearance of the arterial coefficient-derived image (FIG. 14C) andvenous coefficient-derived image (FIG. 14D) which show black areagenerally corresponding to the pressure circles. This abnormal absenceof arterial and venous activity is not visible from the maximumperfusion image alone (FIG. 14B).

After 2 hours, as shown in the color image of FIG. 15A, reduced rednesson the affected areas of the skin surface of the animal can be seen.Arterial influx into the wounds is still almost non-existent, asindicated by the black regions generally corresponding to the pressurewound as shown in the arterial coefficient-derived image of FIG. 15C.However, there is increased venous activity around the center of thewounds, as shown in the venous coefficient-derived image of FIG. 15D. Asearlier, the maximum perfusion image (FIG. 15B) failed to show anyabnormality. Note that a significant difference between the effluxvenous pattern in the severe wound example (FIG. 15D) and the effluxvenous pattern in the minor wound example (FIG. 12D) is that the severewound example exhibits a partial “halo pattern” around the wounds whilethe minor wound example exhibits a “filled circle” pattern around thewounds.

After 24 hours, as shown in the color image of FIG. 16A, only minorabnormalities in skin color of the animal are externally visible. Theseminor skin discolorations are accompanied by dramatically increasedarterial and venous activity around the wounds, as shown in the arterialcoefficient-derived image (FIG. 16C) and venous coefficient-derivedimage (FIG. 16D). However, again, no abnormalities are detectable in themaximum perfusion image (FIG. 16B). Furthermore, the efflux pattern ofvenous activity in FIG. 16D now forms a complete ring enclosing aroundeach of the wounds.

After 48 hours, as shown in the color image of FIG. 17A, only minorabnormalities still remain in the skin color of the animal. Someincreased arterial activity around the wounds is apparent in thearterial coefficient-derived image (FIG. 17C). The efflux pattern ofvenous activity in the venous coefficient-derived image (FIG. 17D) hastransformed into the “filled circle” pattern similar to that observedduring the early stages of the minor wound example (e.g., FIG. 11C).However, only minor abnormalities are detectable in the maximumperfusion image (FIG. 17B).

After 72 hours, there is a noticeable decline in the visual appearanceof the skin surface of the animal as shown in the color image of FIG.18A, accompanied by extreme influx arterial activity as shown in thearterial coefficient-derived image of FIG. 18C. In contrast, as shown inthe venous coefficient-derived image of FIG. 18D, efflux venous activityhas decreased in both the size and intensity of the abnormal areas, andthere is convergence of efflux venous activity toward the center one ofthe wounds (the wound on the righthand side of the image). Again, onlyminor abnormalities are detectable in the maximum perfusion image (FIG.18B).

After 8 days, as shown in the color image of FIG. 19A, a significantimprovement in the appearance of the skin surface of the animal isapparent. There is still an increase in influx arterial activity in theaffected tissue as shown in the arterial coefficient-derived image ofFIG. 19C, while the venous activity pattern shown in the venouscoefficient-derived image of FIG. 19D has almost collapsed into a smallbright region in the center of the wound.

B. Clinical Data Application to Wound Management

Observations from the in vivo pre-clinical data were evaluated andapplied in the context of assessing chronic wounds in human subjects. Asingle, individual coefficient-derived image provides an indication of astate of the wound (e.g., severity, activity of the wound) and can beused alone or in combination with qualitative visualization tofacilitate, for example, an enhanced diagnosis and to assess theeffectiveness of any care strategies.

Example 4 Severe Non-Healing Shin Ulcer (Stage 1 of Wound HealingPresenting a Partial Ring Pattern)

As shown in FIGS. 18A-18C, exemplary results were generated relating toan application of the methods and systems described herein to assesstissue of a subject, particularly for wound management of a severenon-healing shin ulcer. The color image in FIG. 20A shows a woundexternally observed during an assessment of the patient by a clinician.Maximum perfusion images of the wound were generated (not shown) usingLUNA® fluorescence imaging system (available from NOVADAQ® TechnologiesInc.) and ICG as the fluorescence imaging agent. The arterialcoefficient-derived image of FIG. 20B and the venous coefficient-derivedimage of FIG. 20C show the wound pattern with respect to influx arterialactivity and efflux venous activity, respectively. The patterns shown inFIGS. 20B and 20C are consistent with the indicators of the partial ringefflux pattern observed in connection with the pre-clinical in vivoexperiments discussed above. As was discussed above, this pattern iscorrelated with the highest degree of tissue damage and is the farthestaway from healing, which is apparent in this clinical case. As is shownin FIG. 20A, redness appears on the affected areas of the skin. Aclinician looking at FIG. 20A alone would get some visual indicationthat the wound is severe, but it would not be clear from this visualassessment whether this is a non-healing wound or whether it has thepotential to heal. However, the venous coefficient-derived image in FIG.20C shows a partial ring adjacent the wound forming a halo adjacent thewound, which is indicative of increased venous activity and aids theclinician in clearly classifying the wound as having highly damagedtissue and being far from healing.

Example 5 Traumatic Fracture Wound (Stage 2 of Wound Healing Presentinga Complete Ring Pattern)

As shown in FIGS. 21A-21C, exemplary results were generated relating toan application of the methods and systems described herein to assesstissue of a subject, particularly for wound management of a traumaticfracture wound. The patient was a 72-year-old male who incurred atraumatic, compound bimalleolar fracture of his left ankle that requiredoperative repair with an open reduction/internal-fixation procedure. Thesurgical site has become fully disrupted, threatening the fixationplates and hence the extremity. Hyperbaric oxygen therapy (HBOT) therapywas recommended. The color image in FIG. 21A shows a wound observedduring an initial assessment of the patient with the clinician prior toany therapy applied to the wound. Maximum perfusion images of the woundwere generated (not shown) using LUNA® fluorescence imaging system(available from NOVADAQ® Technologies Inc.) and ICG as the fluorescenceimaging agent. The arterial coefficient-derived image of FIG. 21B andthe venous coefficient-derived image of FIG. 21C show the wound patternwith respect to influx arterial activity and efflux venous activity,respectively. The patterns shown in FIGS. 21B and 21C are consistentwith the indicators of the complete ring pattern observed in connectionwith the pre-clinical in vivo experiments discussed above. As wasdiscussed above, this complete ring pattern is correlated with a severecompromise to the tissue perfusion but with the potential to heal withtime, in contrast with the partial ring pattern exhibited by the severewound discussed in Example 4.

Example 6 Ischemic Wound (Stage 3 of Wound Healing Presenting a FilledCircle Pattern)

As shown in FIGS. 22A-22C, exemplary results were generated relating toan application of the methods and systems described herein to assesstissue of a subject, particularly for wound management of an ischemicwound. The patient was 51-year-old male with a left foot ischemic woundwith an amputated metatarsal with osteomyelitis and ascending fasciitis,and obliterative end arteritis. Refractory to aggressive topical careand antibiotics treatments were applied. HBOT was recommended andstarted. FIG. 22A is a color image of the wound during an initialassessment and FIGS. 22B and 22C are the corresponding arterial andvenous coefficient-derived images respectively. As illustrated in thevenous coefficient-derived image in FIG. 22C, the efflux ring issubstantially closed to form the “filled circle” pattern, which isobserved in relatively minor perfusion compromise and indicates that thewound is in an active state of healing.

Example 7 Ischemic Wound (Stage 4 of Wound Healing Presenting aCollapsed Circle Pattern)

As shown in FIGS. 23A-23B, exemplary results were generated relating toan application of the methods and systems described herein to assesstissue of a subject, particularly for follow-up wound management of theischemic wound in Example 6. The color image (FIG. 23A), arterialcoefficient-derived image (FIG. 23B), and venous coefficient-derivedimage (FIG. 23C) were generated for the patient in Example 6 one monthafter the same for Example 6 were generated. It is evident from FIG. 23Cthat the venous activity has almost returned to the normal pattern oftissue that is in its final stages of healing or is healed withuncompromised perfusion, which would not have been apparent from thecolor image in FIG. 23A.

C. Clinical Data Application to Plastic and Reconstructive SurgeryExample 8 Mastectomy (Predictability of Necrotic Tissue Based onCoefficient-Derived Images)

As shown in FIGS. 24A-24D, exemplary results were generated relating toan application of the methods and systems described herein to assesstissue of a subject in plastic and reconstructive breast surgeryprocedures. Data was collected prior to and following a mastectomyperformed on a patient. In particular, a pre-incision maximum perfusionimage (FIG. 24A) of the tissue was generated using SPY® Elitefluorescence imaging system (available from NOVADAQ® Technologies Inc.),where ICG was used as the fluorescence imaging agent. FIGS. 24B and 24Care the corresponding arterial and venous coefficient-derived imagesrespectively. The pre-incision, coefficient-derived images of FIGS. 24Band 24C predictively indicates that tissue in region 2410 of the breastappears to be compromise prior to surgery. However, the correspondingpre-incision, maximum perfusion image of FIG. 24A fails to enable such aprediction in tissue compromise.

FIG. 24D shows a color image of the breast one monthpost-reconstruction, with a necrotic tissue region that developedgenerally in the region 2410 that was previously identified using thecoefficient-derived images as being compromised. Thus, the predictiveinformation provided by the coefficient-derived data could have beenused to guide the surgical strategy in this case to minimizepost-surgical complications.

D. Clinical Data Application to Visualization of a Vessel NetworkExample 9 Vessels in Skin

As shown in FIG. 25, exemplary results were generated relating to anapplication of the methods and systems described herein to assess tissueof a subject in visualization of a vessel network in skin. The patient(same as in Example 5) was a 72-year-old male who incurred a traumatic,compound bimalleolar fracture of his left ankle that required operativerepair with an open reduction/internal-fixation procedure. The surgicalsite has become fully disrupted, threatening the fixation plates andhence the extremity. The venous coefficient-derived image of FIG. 25visualizes a network of vessels (indicated by arrows) in the skin.

Example 10 Vessel Network in a Foot

FIGS. 26A and 26B illustrate exemplary clinical results generatedrelating to an application of the methods and systems described hereinto identify a vessel network and discriminate between different kinds ofvessels in the network. In particular, FIG. 26A is a maximum perfusionimage of a healthy foot of a subject. Although tissue perfusion in thisimage is generally visible, there is limited detail in connection withthe vessel network. In contrast, FIG. 26B, which is the correspondingvenous coefficient-derived image, provides not only a more detailedvisualization of the vessel network but also discriminates betweendifferent kinds of vessels as is illustrated by different brightnesslevels of the vessels in the image (indicated by arrows).

Other Variations

Generally, in one variation, a computer-implemented method of assessinga tissue of a subject includes providing a mathematical modelapproximating a signal intensity arising from an imaging agentcirculating with blood and transiting vasculature of the tissue as afunction of time; calculating a coefficient for the mathematical modelat one or more points on the tissue using empirical signal intensitydata for the imaging agent in the tissue, the empirical signal intensitydata comprising a set of intensity values over time; and generating acoefficient-derived image of the tissue from a plurality of thecoefficients, wherein a difference in the coefficients correlates with adifference in dynamics of the imaging agent in the tissue. Thedifference in the coefficients may comprise a difference in a visualpattern in the coefficient-derived image, and the signal intensityresulting from the transit of the imaging agent through vasculature ofthe tissue may be represented by a time-intensity curve.

In some variations, the coefficient within the mathematical modelcharacterizes a shape of the time-intensity curve. For instance, theshape of the time-intensity curve comprises a region of increasing slopeof the time-intensity curve, a region of decreasing slope of thetime-intensity curve, or a combination thereof. The region of increasingslope of the time-intensity curve may occur from start of measurement ofthe transit of the imaging agent though the vasculature of the tissue toa maximum intensity of the empirical signal intensity data, and theregion of decreasing slope of the time-intensity curve may occur from amaximum intensity of the empirical signal intensity data to end ofmeasurement of the transit of the imaging agent through the vasculatureof the tissue. Furthermore, the region of increasing slope may representan arterial phase of the curve and the region of decreasing slope mayrepresent a venous phase of the curve.

In one particular variation, the method may utilize the mathematicalmodel of

${f(t)} = {{f_{{Ma}\; x}( {1 - ^{- \frac{t^{\prime}}{C_{Inf}}}} )}^{- \frac{t^{\prime}}{C_{Eff}}}}$

where

-   -   f(t)=signal intensity at time t    -   f_(Max)=maximum intensity;    -   t′=t−t_(Lag);    -   t_(Lag)=influx lag time;    -   C_(Inf)=influx coefficient; and    -   C_(Eff)=efflux coefficient.

In these variations, C_(Inf) represents the region of increasing slopeof the time-intensity curve, and C_(Eff) represents the region ofdecreasing slope of the time-intensity curve.

Generating the coefficient-derived image may comprise assigning a pixelintensity to each coefficient in the plurality of the coefficients,scaling the assigned pixel intensities, and or applying histogramequalization to the assigned pixel intensities. As a result, thecoefficient-derived image may comprise an arterial coefficient-derivedimage generated from a plurality of C_(Inf) coefficients, a venouscoefficient-derived image generated from a plurality of C_(Eff)coefficients, or a combination of the arterial coefficient-derived imageand the venous coefficient-derived image.

In some variations, a heterogeneous pattern in the coefficient-derivedimage is indicative of an actual or suspected wound, and the method mayfurther comprise processing the heterogeneous pattern to determine ahealing status of the actual or suspected wound. In some variations, thecoefficient-derived image may additionally or alternatively represent aqualitative profile of the tissue. Furthermore, the coefficient-derivedimage may facilitate visualization of tissue perfusion in the tissue,prognostic information of wound healing, visualization of anatomicalshape of the tissue (e.g., visualization of a vessel, a vessel network,or a combination thereof).

The method may further comprise tracking a change in thecoefficient-derived image over time to assess progress of healing of thetissue, efficacy of clinical intervention, or a combination thereof. Forinstance, the method may comprise quantifying a selected region of thecoefficient-derived image to provide a quantitative indicator of theprogress of healing of the tissue, efficacy of clinical intervention, orthe combination thereof. Quantifying the selected region may, forexample, comprise calculating of an area for the selected region, andprocessing the area for the selected region of a firstcoefficient-derived image using the area for the selected region of asecond coefficient-derived image to provide the quantitative indicator.Such processing may comprise dividing the area for the selected regionof the first coefficient-derived image by the area for the selectedregion of the second coefficient-derived image. As a result, thequantitative indicator may be indicative of an ongoing process ofhealing of the tissue.

In some variations, assessing the tissue of the subject comprisesassessing a wound in the tissue, a peri-wound in the tissue, or acombination thereof (e.g., assessing a state of the wound, a property ofthe wound, a condition of the wound, a healing status of the wound, or acombination thereof, where the state of the wound, the property of thewound, the condition of the wound, or the healing status of the woundcomprises inflammation, malignancy, abnormality, disease, or acombination thereof). For example, the wound may comprises an injury tothe tissue, such as a surgical wound, a chronic wound, an acute wound,or a combination thereof (e.g., an incision, a pressure ulcer, alaceration, an abrasion, a puncture, a contusion, an avulsion, a cavity,a burn, a pressure ulcer, a venous ulcer, an arterial ulcer, a diabeticlower extremity ulcer, or a combination thereof).

The imaging agent may include a fluorescence imaging agent, where theempirical signal intensity data is data derived from fluorescenceimaging acquired using a fluorescence imaging system. For instance, thefluorescence imaging agent may be administered to the subjectimmediately prior to acquisition of the empirical signal intensity data.The fluorescence imaging agent comprises a fluorescence dye, an analoguethereof, a derivative thereof, or a combination thereof. For example,the fluorescence dye may comprise a tricarbocyanine dye, such asindocyanine green (ICG). As another example, the fluorescence dye maycomprise fluorescein isothiocyanate, rhodamine, phycoerythrin,phycocyanin, allophycocyanin, o-phthaldehyde, fluorescamine, roseBengal, trypan blue, fluoro-gold, methylene blue, or a combinationthereof.

Also disclosed herein is use of the above-described methods todiscriminate between a healing wound and a non-healing wound, use of theabove-described methods to provide information e.g. usable in clinicaldecision making, use of the above-described methods in clinical decisionmaking regarding continuation of treatment, and/or use of theabove-described methods in wound management, plastic surgery,reconstructive surgery, or a combination thereof. Furthermore, disclosedherein is use of the coefficient, the coefficient-derived image, or bothfor machine learning. Furthermore, disclosed herein is the use of thearterial coefficient-derived image and the venous coefficient-derivedimage for predicting a healing potential of a wound.

Generally, in another variation, there is disclosed acomputer-implemented method of providing (data usable in) a prognosisfor wound healing in tissue of a subject, the tissue comprising a wound,the method comprising: generating a time-intensity curve for acalculation region in a time series of fluorescence empirical signalintensity data obtained from the tissue, the time series of fluorescenceempirical signal intensity data capturing transit of a fluorescenceimaging agent through vasculature of the tissue as a function of time;processing the time-intensity curve to calculate an influx coefficientapproximating an arterial portion of the time-intensity curve and anefflux coefficient approximating a venous portion of the time-intensitycurve; generating an arterial coefficient-derived image of the tissuefrom a plurality of the influx coefficients and a venouscoefficient-derived image from a plurality of the efflux coefficients,wherein the arterial coefficient-derived image comprises a first regionrepresenting the wound and the venous coefficient map comprises a secondregion representing the wound; and assessing the first region relativeto the second region to derive an indicator of a progress of healing. Insome variations, assessing the first region relative to the secondregion to derive the indicator of the progress of healing comprisescalculating a first area for the first region and a second area for thesecond region; and comparing the first and second areas.

Generally, in another variations, there is disclosed acomputer-implemented method operating with an imaging system, theimaging system configured to capture the transit of an imaging agentover time through the tissue wherein the system processor: utilizes amathematical model approximating a signal intensity arising from animaging agent circulating with blood and transiting vasculature of thetissue as a function of time to calculate a coefficient for themathematical model at one or more points on the tissue using empiricalsignal intensity data for the imaging agent in the tissue, the empiricalsignal intensity data comprising a set of intensity values over time;and generates a coefficient-derived image of the tissue from a pluralityof the coefficients, wherein a difference in the coefficients correlateswith a difference in a dynamic behavior of the imaging agent in thetissue.

Generally, in another variation, there is disclosed a tangiblenon-transitory computer readable medium having computer-executableprogram code means embedded thereon comprising a method of assessing atissue of a subject, the method comprising: providing a mathematicalmodel approximating a signal intensity arising from an imaging agentcirculating with blood and transiting vasculature of the tissue as afunction of time; calculating a coefficient for the mathematical modelat one or more points on the tissue using empirical signal intensitydata for the imaging agent in the tissue, the empirical signal intensitydata comprising a set of intensity values over time; and generating acoefficient-derived image of the tissue from a plurality of thecoefficients, wherein a difference in the coefficients correlates with adifference in a dynamic behavior of the imaging agent in the tissue.

Generally, in one variation of a system for assessing a tissue of asubject, the system comprises a user interface; a processor configuredto communicate with the user interface; and a non-transitorycomputer-readable storage medium having instructions stored. When theinstructions are executed by the processor, the instructions cause theprocessor to perform operations including: utilizing a mathematicalmodel approximating a signal intensity arising from an imaging agentcirculating with blood and transiting vasculature of the tissue as afunction of time; calculating a coefficient for the mathematical modelat one or more points on the tissue using empirical signal intensitydata for the imaging agent in the tissue, the empirical signal intensitydata comprising a set of intensity values over time; and generating acoefficient-derived image of the tissue from a plurality of thecoefficients, wherein a difference in the coefficients correlates with adifference in a dynamic behavior of the imaging agent in the tissue.

While the present disclosure has been illustrated and described inconnection with various embodiments shown and described in detail, it isnot intended to be limited to the details shown, since variousmodifications and structural changes may be made without departing inany way from the scope of the present disclosure. Various modificationsof form, arrangement of components, steps, details and order ofoperations of the embodiments illustrated, as well as other embodimentsof the disclosure may be made without departing in any way from thescope of the present disclosure, and will be apparent to a person ofskill in the art upon reference to this description. It is thereforecontemplated that the appended claims will cover such modifications andembodiments as they fall within the true scope of the disclosure. Forthe purpose of clarity and a concise description features are describedherein as part of the same or separate embodiments, however, it will beappreciated that the scope of the disclosure includes embodiments havingcombinations of all or some of the features described. For the terms“for example” and “such as,” and grammatical equivalences thereof, thephrase “and without limitation” is understood to follow unlessexplicitly stated otherwise. As used herein, the singular forms “a”,“an”, and “the” include plural referents unless the context clearlydictates otherwise.

What is claimed is:
 1. A system for allowing assessment of tissue of asubject, the system comprising: one or more processors; and memoryhaving instructions stored thereon, the instructions, when executed bythe one or more processors, cause the system to: receive a time seriesof signal intensity data capturing transit of an imaging agent throughtissue over a period of time, wherein the time series of signalintensity data define a plurality of calculation regions and whereinsignal intensity in each calculation region over the period of time maybe approximated by a time-intensity curve corresponding to thatcalculation region; determine, for each calculation region, acoefficient value that is related to at least a portion of thetime-intensity curve corresponding to the calculation region; andconvert the coefficient values across the plurality of calculationregions into a coefficient-derived image map.
 2. The system of claim 1,wherein at least one calculation region is defined by one pixel or onevoxel.
 3. The system of claim 1, wherein the coefficient valuecharacterizes a shape of the time-intensity curve.
 4. The system ofclaim 3, wherein the coefficient value characterizes a region ofincreasing slope of the time-intensity curve, a region of decreasingslope of the time-intensity curve, or a combination thereof.
 5. Thesystem of claim 4, wherein the region of increasing slope of thetime-intensity curve represents an arterial phase of the time-intensitycurve and the region of decreasing slope of the time-intensity curverepresents a venous phase of the time-intensity curve.
 6. The system ofclaim 1, wherein the coefficient-derived image map is based on acorrelation of each of the coefficient values with a respective pixelintensity.
 7. The system of claim 1, wherein a heterogeneous pattern inthe coefficient-derived image map is indicative of an actual orsuspected wound.
 8. The system of claim 1, wherein thecoefficient-derived image map allows for, or provides information foruse in, predictive assessment of healing of tissue of the subject. 9.The system of claim 1, further comprising a display, wherein theinstructions cause the system to display the coefficient-derived imagemap on the display.
 10. The system of claim 9, wherein the instructionscause the system to superimpose the coefficient-derived image map on ananatomical image of the tissue on the display.
 11. The system of claim1, further comprising (i) a light source that provides an excitationlight to induce fluorescence emission from the imaging agent in thetissue; (ii) an image acquisition assembly that generates the timeseries of signal intensity data based on the fluorescence emission,(iii) or a combination thereof.
 12. A method for use in medical imagingfor assessing tissue of a subject, the method comprising: at a computersystem including one or more processors and memory, receiving a timeseries of signal intensity data capturing transit of an imaging agentthrough tissue over a period of time, wherein the tissue comprises aplurality of calculation regions and wherein signal intensity in eachcalculation region over the period of time may be approximated by atime-intensity curve corresponding to the calculation region;determining, for each calculation region, a coefficient value that isrelated to at least a portion of the time-intensity curve correspondingto the calculation region; and converting the coefficient values acrossthe plurality of calculation regions into a coefficient-derived imagemap.
 13. The method of claim 12, wherein at least one calculation regionis defined by one pixel or one voxel.
 14. The method of claim 12,wherein the coefficient value characterizes a shape of thetime-intensity curve.
 15. The method of claim 14, wherein thecoefficient value characterizes a region of increasing slope of thetime-intensity curve, a region of decreasing slope of the time-intensitycurve, or a combination thereof.
 16. The method of claim 15, wherein theregion of increasing slope of the time-intensity curve represents anarterial phase of the time-intensity curve and the region of decreasingslope of the time-intensity curve represents a venous phase of thetime-intensity curve.
 17. The method of claim 12, wherein converting thecoefficient values into a coefficient-derived image map comprisescorrelating each coefficient value with an intensity value.
 18. Themethod of claim 12, further comprising displaying thecoefficient-derived image map on a display.
 19. The method of claim 18,further comprising superimposing the coefficient-derived image map on ananatomical image of the tissue on the display.
 20. The method of claim12, wherein the assessing of tissue of the subject is based at least inpart on the coefficient-derived image map.
 21. The method of claim 20,wherein the assessing of tissue of the subject comprises assessing awound in the tissue, a peri-wound in the tissue, or a combinationthereof.
 22. The method of claim 21, wherein assessing the wound in thetissue, the peri-wound in the tissue, or the combination thereofcomprises assessing a state of the tissue, a property of the tissue, acondition of the tissue, a healing status of the tissue, or acombination thereof.
 23. The method of claim 20, wherein the assessingof tissue of the subject comprises assessing a healing status of a woundbased on a heterogeneous pattern in the coefficient-derived image map.24. The method of claim 20, wherein the assessing of tissue of thesubject comprises generating a quantitative predictor of the progress ofhealing of tissue, efficacy of clinical intervention, or a combinationthereof, based on at least a portion of the coefficient-derived image.25. The method of claim 20, further comprising comparing a plurality ofcoefficient-derived image maps that are based on a plurality of timeseries of signal intensity data captured over time, and assessingprogress of healing of tissue, efficacy of clinical intervention, or acombination thereof based on the comparison of the plurality ofcoefficient-derived image maps.
 26. The method of claim 20, furthercomprising: determining, for each calculation region, a secondcoefficient value that is related to at least a second portion of thetime-intensity curve corresponding to the calculation region; andconverting the second coefficient values across the plurality ofcalculation regions into a second coefficient-derived image map.
 27. Themethod of claim 26, wherein the first coefficient-derived image map isan arterial coefficient-derived image map and the secondcoefficient-derived image map is a venous coefficient-derived image map.28. The method of claim 26, wherein the assessing of tissue of thesubject comprises generating a quantitative predictor of the progress ofhealing of tissue, efficacy of clinical intervention, or a combinationthereof, based on the first and second coefficient-derived image maps.29. The method of claim 28, wherein generating a quantitative predictorcomprises comparing the area of a first selected region in the firstcoefficient-derived image map to the area of a second selected region inthe second coefficient-derived image map.
 30. The method of claim 29,wherein the first and second selected regions represent an actual orsuspected wound.
 31. The method of claim 29, wherein comparing the areaof the first selected region and the area of the second selected regioncomprises determining a ratio of the area of the first selected regionand the second selected region.
 32. The method of claim 12, furthercomprising generating the time series of signal intensity data using afluorescence imaging system that captures transit of the imaging agentthrough tissue over a period of time.
 33. The method of claim 12,wherein the imaging agent comprises indocyanine green, fluoresceinisothiocyanate, rhodamine, phycoerythrin, phycocyanin, allophycocyanin,ophthaldehyde, fluorescamine, rose Bengal, trypan blue, fluoro-gold,green fluorescence protein, a flavin, methylene blue, porphysomes,cyanine dye, IRDDye800CW, CLR 1502 combined with a targeting ligand,OTL38 combined with a targeting ligand, or a combination thereof.
 34. Akit comprising the system of claim 1 and an imaging agent.
 35. The kitof claim 34, wherein the imaging agent comprises a fluorescence imagingagent.
 36. The kit of claim 35, wherein the fluorescence imaging agentcomprises indocyanine green, fluorescein isothiocyanate, rhodamine,phycoerythrin, phycocyanin, allophycocyanin, ophthaldehyde,fluorescamine, rose Bengal, trypan blue, fluoro-gold, green fluorescenceprotein, a flavin, methylene blue, porphysomes, cyanine dye,IRDDye800CW, CLR 1502 combined with a targeting ligand, OTL38 combinedwith a targeting ligand, or a combination thereof.
 37. The kit of claim34 further comprising a tangible non-transitory computer readable mediumhaving computer-executable program code embedded thereon, the computerexecutable program code providing instructions for causing one or moreprocessors, when executing the instructions, to perform the method ofreceiving a time series of signal intensity data capturing transit of animaging agent through tissue over a period of time, wherein the tissuecomprises a plurality of calculation regions and wherein signalintensity in each calculation region over the period of time may beapproximated by a time-intensity curve corresponding to the calculationregion; determining, for each calculation region, a coefficient valuethat is related to at least a portion of the time-intensity curvecorresponding to the calculation region; and converting the coefficientvalues across the plurality of calculation regions into acoefficient-derived image map.
 38. The kit of claim 37 furthercomprising instructions for installing the computer-executable programcode.
 39. A fluorescence imaging agent for use in the system of claim 1.40. A fluorescence imaging agent for use in the method of claim
 12. 41.A method for visualizing transit of an imaging agent through tissue of asubject, the method comprising: at a computer system including one ormore processors and memory, receiving a time series of signal intensitydata capturing the transit of the imaging agent through the tissue overa period of time, wherein the tissue comprises a plurality ofcalculation regions and wherein signal intensity in each calculationregion over the period of time may be approximated by a time-intensitycurve corresponding to the calculation region; determining, for eachcalculation region, a coefficient value that is related to at least aportion of the time-intensity curve corresponding to the calculationregion; and converting the coefficient values across the plurality ofcalculation regions into a coefficient-derived image map.
 42. The methodof claim 41, wherein at least one calculation region is defined by onepixel or one voxel.
 43. The method of claim 41, wherein the coefficientvalue characterizes a shape of the time-intensity curve.
 44. The methodof claim 43, wherein the coefficient value characterizes a region ofincreasing slope of the time-intensity curve, a region of decreasingslope of the time-intensity curve, or a combination thereof.
 45. Themethod of claim 41, further comprising displaying thecoefficient-derived image map on a display.
 46. The method of claim 45,wherein the coefficient-derived image map is a still image.
 47. Themethod of claim 45, further comprising superimposing thecoefficient-derived image map on an anatomical image of the tissue onthe display.